{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "import numpy as np\n",
    "\n",
    "(train_images, train_labels), _ = mnist.load_data()\n",
    "train_images = train_images.reshape((60000, 28 * 28))\n",
    "train_images = train_images.astype(\"float32\") / 255\n",
    "\n",
    "train_images_with_noise_channels = np.concatenate(\n",
    "    [train_images, np.random.random((len(train_images), 784))], axis=1)\n",
    "train_images_with_zeros_channels = np.concatenate(\n",
    "    [train_images, np.zeros((len(train_images), 784))], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115261"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3277893-3162632"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.6959 - loss: 1.0398 - val_accuracy: 0.9014 - val_loss: 0.3286\n",
      "Epoch 2/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9113 - loss: 0.2935 - val_accuracy: 0.9323 - val_loss: 0.2287\n",
      "Epoch 3/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9435 - loss: 0.1856 - val_accuracy: 0.9451 - val_loss: 0.1845\n",
      "Epoch 4/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9604 - loss: 0.1300 - val_accuracy: 0.9535 - val_loss: 0.1496\n",
      "Epoch 5/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9708 - loss: 0.0938 - val_accuracy: 0.9477 - val_loss: 0.1721\n",
      "Epoch 6/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9788 - loss: 0.0681 - val_accuracy: 0.9556 - val_loss: 0.1521\n",
      "Epoch 7/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9840 - loss: 0.0507 - val_accuracy: 0.9638 - val_loss: 0.1256\n",
      "Epoch 8/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9884 - loss: 0.0385 - val_accuracy: 0.9657 - val_loss: 0.1327\n",
      "Epoch 9/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0283 - val_accuracy: 0.9622 - val_loss: 0.1504\n",
      "Epoch 10/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9949 - loss: 0.0195 - val_accuracy: 0.9641 - val_loss: 0.1402\n",
      "Epoch 1/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.8583 - loss: 0.4838 - val_accuracy: 0.9548 - val_loss: 0.1577\n",
      "Epoch 2/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9613 - loss: 0.1342 - val_accuracy: 0.9676 - val_loss: 0.1090\n",
      "Epoch 3/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9768 - loss: 0.0825 - val_accuracy: 0.9696 - val_loss: 0.1064\n",
      "Epoch 4/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9837 - loss: 0.0576 - val_accuracy: 0.9758 - val_loss: 0.0787\n",
      "Epoch 5/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9890 - loss: 0.0416 - val_accuracy: 0.9743 - val_loss: 0.0839\n",
      "Epoch 6/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9917 - loss: 0.0295 - val_accuracy: 0.9801 - val_loss: 0.0678\n",
      "Epoch 7/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9938 - loss: 0.0224 - val_accuracy: 0.9790 - val_loss: 0.0712\n",
      "Epoch 8/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9956 - loss: 0.0172 - val_accuracy: 0.9807 - val_loss: 0.0737\n",
      "Epoch 9/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9970 - loss: 0.0126 - val_accuracy: 0.9809 - val_loss: 0.0695\n",
      "Epoch 10/10\n",
      "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9974 - loss: 0.0107 - val_accuracy: 0.9808 - val_loss: 0.0696\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "def get_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(512, activation=\"relu\"),\n",
    "        layers.Dense(10, activation=\"softmax\")\n",
    "    ])\n",
    "    model.compile(optimizer=\"rmsprop\",\n",
    "                  loss=\"sparse_categorical_crossentropy\",\n",
    "                  metrics=[\"accuracy\"])\n",
    "    return model\n",
    "\n",
    "model = get_model()\n",
    "history_noise = model.fit(\n",
    "    train_images_with_noise_channels, train_labels,\n",
    "    epochs=10,\n",
    "    batch_size=128,\n",
    "    validation_split=0.2)\n",
    "\n",
    "model = get_model()\n",
    "history_zeros = model.fit(\n",
    "    train_images_with_zeros_channels, train_labels,\n",
    "    epochs=10,\n",
    "    batch_size=128,\n",
    "    validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b43b840830>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "val_acc_noise = history_noise.history[\"val_accuracy\"]\n",
    "val_acc_zeros = history_zeros.history[\"val_accuracy\"]\n",
    "epochs = range(1, 11)\n",
    "plt.plot(epochs, val_acc_noise, \"b-\",\n",
    "         label=\"Validation accuracy with noise channels\")\n",
    "plt.plot(epochs, val_acc_zeros, \"b--\",\n",
    "         label=\"Validation accuracy with zeros channels\")\n",
    "plt.title(\"Effect of noise channels on validation accuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
